Event Overview
The attributes of FAIR data facilitate, amongst others, an easier reproducibility of research experiments, and allow the more efficient linking of datasets from different research groups, institutes, and even scientific fields.
This workshop presents a software framework and a workflow built to expand a prior proof-of-concept and to turn it into a daily lab routine. Its purpose is to enable the generation, storage, and analysis of FAIR datasets in the field of materials tribology, as well as materials science in general.
An important piece of the framework is a software tool called VocPopuli. It enables the development of FAIR controlled vocabularies in a collaborative fashion. These vocabularies describe the experiments of interest for a given group, as well as all other equipment, processes, and data which pertain to them. Afterwards, the vocabularies can be used by the various other tools, which have been developed as part of the presented work. Out of these, the workshop will focus mainly on FS-DigitalBook. This is a tablet-based application, which allows experimentalists to describe processes and objects relevant to their field of research in a FAIR fashion, without leaving the lab bench. The application utilizes the vocabularies developed with the help of VocPopuli as metadata schemata. These structures are used to create data input forms which can afterwards be filled with specific values, linked with files generated by the procedure at hand, and stored in a laboratory database. Finally, the generated data records can be analyzed using another piece of our software suite – the FS-Report.
Speaker: Ilia Bagov, Institute of Applied Materials Science, KIT
Ilia Bagov is a Research Associate working at the Institute of Applied Materials at the Karlsruhe Institute of Technology with prior experience in machine learning in areas such as remote sensing, finance, recommender systems, and more.
He specializes in the convergence of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles within the realm of experimental sciences. Ilia has played a key role in leading software development teams responsible for the creation of innovative applications, including VocPopuli and the FAIR-Save suite of solutions, designed specifically to annotate and generate FAIR-compliant data in experimental settings.
Currently, Ilia is focused on the development of a comprehensive framework that seamlessly integrates FAIR datasets into machine learning algorithms. This undertaking aims to unlock the full potential of machine learning by harnessing the richness and reliability inherent in FAIR data.
